[Re] The Discriminative Kalman Filter for Bayesian Filtering with Nonlinear and Non-Gaussian Observation Models
A replication about Bayesian filtering with nonlinear, non-Gaussian observations using a discriminative Kalman filter.
External publication. Published by its original venue; not published in our journal.
How do filtering error and interval coverage change as observation noise departs from a Gaussian model?
Generate latent states and observations with an evaluator-owned process, compare to a simple baseline, and recompute errors from estimated trajectories.
What you could produce
- Versioned protocol, input and environment manifest, and independent per-case comparison table including uncertainty and incomplete cases.
Before you use it
- Python and PyTorch (current README)
- A requirements file and preprocessing notebooks are referenced but were not downloaded or evaluated
Limits to keep in view
- No research code was executed; no independent scientific verification has been performed.
- The current qualified pilot is self-contained Python 3.13 with a 90-second author deadline. This article's environment has not been qualified for that path.
- Published source metadata and a historical review do not establish compatibility, successful reproduction, operator independence or current scientific correctness.
Source and permission context
Casco-Rodriguez, Josue; Kemere, Caleb; Baraniuk, Richard G.. [Re] The Discriminative Kalman Filter for Bayesian Filtering with Nonlinear and Non-Gaussian Observation Models. ReScience C 10(1), #3; 10.5281/zenodo.15172014.
Catalog listing reviewed. This review covers the description and source links displayed here.
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Reviewed 2026-09-14. Copying or adapting source files remains subject to their own terms.
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